Hydrology models approach to estimation of the groundwater recharge: case study in the Bulgarian Danube watershed View Full Text


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Article Info

DATE

2018-06

AUTHORS

Olga Nitcheva

ABSTRACT

The groundwater (GW) makes an important part of a region runoff. GW bodies playing the role of accumulating reservoirs regulate the GW discharge enabling the river flow to have more uniform long-term distribution. Along with other important advantages, the GW offers the users stable water abstraction rate independent from the recharge rate. The GW recharge quantification belongs to the uneasy tasks in the water resource management. Applying the conventional methods needs multiyear observation records of the variation of the groundwater body (GWB) characteristics. The employment of hydrology models avoids that necessity but requires great amount of data related to the soil hydraulic properties, the land topography and cover of the GWB watershed and long-term records of the climatic effects. The paper presents an introduction of the mathematical model CLM3 into the GW recharge estimation problem. It is a complex and advanced model with adequate interpretation of the water-related processes in the soil and on the land surface under atmospheric effects. The input is available from NCEP/NCAR reanalysis atmosphere data and the International Geosphere-Biosphere Program (IGBP) data base. The model is applied to GW recharge assessment of the Bulgarian Danube district for the year 2013. The obtained monthly and yearly total district values and the areal distribution of the infiltration intensity are matched to the existing field observation-based estimates. The study shows that the CLM3 model approach leads to encouraging results. The method comes very useful with GWB lacking regime observation data as well as for GW recharge prognostic assessments under climatic scenarios. More... »

PAGES

464

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12665-018-7605-1

DOI

http://dx.doi.org/10.1007/s12665-018-7605-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105134551


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